In [1]:
import pandas as pd
In [2]:
df = pd.read_csv('Olympic_data.csv')
df
Out[2]:
ID Name Sex Age Height Weight Team NOC Games Year Season City Sport Event Medal
0 1 A Dijiang M 24.0 180.0 80.0 China CHN 1992 Summer 1992 Summer Barcelona Basketball Basketball Men's Basketball NaN
1 2 A Lamusi M 23.0 170.0 60.0 China CHN 2012 Summer 2012 Summer London Judo Judo Men's Extra-Lightweight NaN
2 3 Gunnar Nielsen Aaby M 24.0 NaN NaN Denmark DEN 1920 Summer 1920 Summer Antwerpen Football Football Men's Football NaN
3 4 Edgar Lindenau Aabye M 34.0 NaN NaN Denmark/Sweden DEN 1900 Summer 1900 Summer Paris Tug-Of-War Tug-Of-War Men's Tug-Of-War Gold
4 5 Christine Jacoba Aaftink F 21.0 185.0 82.0 Netherlands NED 1988 Winter 1988 Winter Calgary Speed Skating Speed Skating Women's 500 metres NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
271111 135569 Andrzej ya M 29.0 179.0 89.0 Poland-1 POL 1976 Winter 1976 Winter Innsbruck Luge Luge Mixed (Men)'s Doubles NaN
271112 135570 Piotr ya M 27.0 176.0 59.0 Poland POL 2014 Winter 2014 Winter Sochi Ski Jumping Ski Jumping Men's Large Hill, Individual NaN
271113 135570 Piotr ya M 27.0 176.0 59.0 Poland POL 2014 Winter 2014 Winter Sochi Ski Jumping Ski Jumping Men's Large Hill, Team NaN
271114 135571 Tomasz Ireneusz ya M 30.0 185.0 96.0 Poland POL 1998 Winter 1998 Winter Nagano Bobsleigh Bobsleigh Men's Four NaN
271115 135571 Tomasz Ireneusz ya M 34.0 185.0 96.0 Poland POL 2002 Winter 2002 Winter Salt Lake City Bobsleigh Bobsleigh Men's Four NaN

271116 rows × 15 columns

In [3]:
df.info() 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 271116 entries, 0 to 271115
Data columns (total 15 columns):
 #   Column  Non-Null Count   Dtype  
---  ------  --------------   -----  
 0   ID      271116 non-null  int64  
 1   Name    271116 non-null  object 
 2   Sex     271116 non-null  object 
 3   Age     261642 non-null  float64
 4   Height  210945 non-null  float64
 5   Weight  208241 non-null  float64
 6   Team    271116 non-null  object 
 7   NOC     271116 non-null  object 
 8   Games   271116 non-null  object 
 9   Year    271116 non-null  int64  
 10  Season  271116 non-null  object 
 11  City    271116 non-null  object 
 12  Sport   271116 non-null  object 
 13  Event   271116 non-null  object 
 14  Medal   39783 non-null   object 
dtypes: float64(3), int64(2), object(10)
memory usage: 31.0+ MB
In [4]:
df.describe()
Out[4]:
ID Age Height Weight Year
count 271116.000000 261642.000000 210945.000000 208241.000000 271116.000000
mean 68248.954396 25.556898 175.338970 70.702393 1978.378480
std 39022.286345 6.393561 10.518462 14.348020 29.877632
min 1.000000 10.000000 127.000000 25.000000 1896.000000
25% 34643.000000 21.000000 168.000000 60.000000 1960.000000
50% 68205.000000 24.000000 175.000000 70.000000 1988.000000
75% 102097.250000 28.000000 183.000000 79.000000 2002.000000
max 135571.000000 97.000000 226.000000 214.000000 2016.000000
  • The minimum age 10 Years and maximun age 97 Years is correct.
In [5]:
df.isnull().sum()
Out[5]:
ID             0
Name           0
Sex            0
Age         9474
Height     60171
Weight     62875
Team           0
NOC            0
Games          0
Year           0
Season         0
City           0
Sport          0
Event          0
Medal     231333
dtype: int64

  1. medal can be replace by 0.

  2. age,height and weight can be replace with average values.

In [6]:
df['Medal']=df.Medal.fillna('no')
df['Age']=df.Age.fillna(25)
df['Height']=df.Height.fillna(175)
df['Weight']=df.Weight.fillna(70)
In [7]:
df.isnull().sum()
Out[7]:
ID        0
Name      0
Sex       0
Age       0
Height    0
Weight    0
Team      0
NOC       0
Games     0
Year      0
Season    0
City      0
Sport     0
Event     0
Medal     0
dtype: int64
In [8]:
df = df.drop(['Name','Games','Event','ID','City'],axis=1)
df
Out[8]:
Sex Age Height Weight Team NOC Year Season Sport Medal
0 M 24.0 180.0 80.0 China CHN 1992 Summer Basketball no
1 M 23.0 170.0 60.0 China CHN 2012 Summer Judo no
2 M 24.0 175.0 70.0 Denmark DEN 1920 Summer Football no
3 M 34.0 175.0 70.0 Denmark/Sweden DEN 1900 Summer Tug-Of-War Gold
4 F 21.0 185.0 82.0 Netherlands NED 1988 Winter Speed Skating no
... ... ... ... ... ... ... ... ... ... ...
271111 M 29.0 179.0 89.0 Poland-1 POL 1976 Winter Luge no
271112 M 27.0 176.0 59.0 Poland POL 2014 Winter Ski Jumping no
271113 M 27.0 176.0 59.0 Poland POL 2014 Winter Ski Jumping no
271114 M 30.0 185.0 96.0 Poland POL 1998 Winter Bobsleigh no
271115 M 34.0 185.0 96.0 Poland POL 2002 Winter Bobsleigh no

271116 rows × 10 columns

  • we have Droped few columns which are not needed.
In [9]:
from sklearn.preprocessing import LabelEncoder
le1 = LabelEncoder()
le2 = LabelEncoder()
le3 = LabelEncoder()
df['le_sex'] =  le1.fit_transform(df.Sex)
df['le_medal']= le2.fit_transform(df.Medal)
df['le_season']= le3.fit_transform(df.Season)
df
Out[9]:
Sex Age Height Weight Team NOC Year Season Sport Medal le_sex le_medal le_season
0 M 24.0 180.0 80.0 China CHN 1992 Summer Basketball no 1 3 0
1 M 23.0 170.0 60.0 China CHN 2012 Summer Judo no 1 3 0
2 M 24.0 175.0 70.0 Denmark DEN 1920 Summer Football no 1 3 0
3 M 34.0 175.0 70.0 Denmark/Sweden DEN 1900 Summer Tug-Of-War Gold 1 1 0
4 F 21.0 185.0 82.0 Netherlands NED 1988 Winter Speed Skating no 0 3 1
... ... ... ... ... ... ... ... ... ... ... ... ... ...
271111 M 29.0 179.0 89.0 Poland-1 POL 1976 Winter Luge no 1 3 1
271112 M 27.0 176.0 59.0 Poland POL 2014 Winter Ski Jumping no 1 3 1
271113 M 27.0 176.0 59.0 Poland POL 2014 Winter Ski Jumping no 1 3 1
271114 M 30.0 185.0 96.0 Poland POL 1998 Winter Bobsleigh no 1 3 1
271115 M 34.0 185.0 96.0 Poland POL 2002 Winter Bobsleigh no 1 3 1

271116 rows × 13 columns

  • We have transform our string data into numeric data.
In [10]:
df.le_medal.unique()
Out[10]:
array([3, 1, 0, 2])

Medal :

  • Gold = 1

  • Silver = 2

  • Bronze = 0

  • Not get medal = 3

Sex :

  • Male = 0

  • Female = 1

Season :

  • Summer = 0

  • Winter = 1

In [11]:
df[df.duplicated()]
df.drop_duplicates(inplace=True)
df
Out[11]:
Sex Age Height Weight Team NOC Year Season Sport Medal le_sex le_medal le_season
0 M 24.0 180.0 80.0 China CHN 1992 Summer Basketball no 1 3 0
1 M 23.0 170.0 60.0 China CHN 2012 Summer Judo no 1 3 0
2 M 24.0 175.0 70.0 Denmark DEN 1920 Summer Football no 1 3 0
3 M 34.0 175.0 70.0 Denmark/Sweden DEN 1900 Summer Tug-Of-War Gold 1 1 0
4 F 21.0 185.0 82.0 Netherlands NED 1988 Winter Speed Skating no 0 3 1
... ... ... ... ... ... ... ... ... ... ... ... ... ...
271110 F 33.0 171.0 69.0 Belarus BLR 2016 Summer Basketball no 0 3 0
271111 M 29.0 179.0 89.0 Poland-1 POL 1976 Winter Luge no 1 3 1
271112 M 27.0 176.0 59.0 Poland POL 2014 Winter Ski Jumping no 1 3 1
271114 M 30.0 185.0 96.0 Poland POL 1998 Winter Bobsleigh no 1 3 1
271115 M 34.0 185.0 96.0 Poland POL 2002 Winter Bobsleigh no 1 3 1

188894 rows × 13 columns

  • Remove the dublicated
In [12]:
df.NOC.unique().shape
Out[12]:
(230,)
In [13]:
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set_style('darkgrid')
import plotly.express as px
In [14]:
px.line(df.Height).update_layout(title="Height of Players",
    xaxis_title="count of players",
    yaxis_title="Height(cm)")
In [15]:
px.histogram(df.Height,nbins=30,title="Height(cm)").update_layout(
    xaxis_title="Height(cm)",
    yaxis_title="Count")
  • The average Height of players is form 175 to 180 cms.
In [16]:
px.line(df.Weight).update_layout(title="Weight of Players",
    xaxis_title="Number of players",
    yaxis_title="Weight(kg)")
In [17]:
px.histogram(df.Weight,nbins=30,title="Weight").update_layout(
    xaxis_title="Weight(kg)",
    yaxis_title="Count")
  • The average Weight of players is form 70 to 80 kgs.
In [18]:
px.histogram(df.Sex,title="Gender (Male X Female)").update_layout(
    xaxis_title="Sex",
    yaxis_title="Count")
In [19]:
px.histogram(df.Medal,title="Medals").update_layout(
    xaxis_title="Medals",
    yaxis_title="Count")
In [20]:
plt.figure(figsize=(15,8))
plt.title("Weight of player(kg)")
plt.xlabel("Height of player(cm) -->")
plt.ylabel("Weight of player(kg)")
plt.scatter(df.Height,df.Weight);
In [21]:
px.histogram(df.Sport).update_layout(xaxis=dict(dtick=5)).update_layout(title='Sports',
    xaxis_title="Total sports in Olympics",
    yaxis_title="Count")
  • Most participation is for Athetics, Swimming, Rowing, Cycling, Shooting...

THANK YOU¶